Unlock the Power of Machine Learning: Learn the Techniques that are Revolutionizing Industry!
Machine Learning is transforming the world as we know it. From improving healthcare to predicting market trends, this innovative technology…
The Future Is Here: The Latest News And Developments In The World Of AI!
Artificial intelligence (AI) is rapidly evolving, and it is becoming an integral part of our daily lives. From business to…
SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
arXiv:2607.18239v1 Announce Type: new Abstract: Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond…
Enhancing Rubric-based RL via Self-Distillation
arXiv:2607.18082v2 Announce Type: replace-cross Abstract: Rubric-based RL has recently shown promise in improving LLMs on open-ended tasks. A widely recognized…
SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
arXiv:2607.18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether…
Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model
arXiv:2607.18958v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their…
Alignment of a Total Automation Economy
arXiv:2607.17015v2 Announce Type: replace-cross Abstract: We consider economic theory from the perspective of a total automation economy, one with no…
Rater State Bias in RLHF Preference Data: An Audit Framework
arXiv:2607.16195v1 Announce Type: new Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels…
When Does Muon Help Agentic Reinforcement Learning?
arXiv:2607.16169v2 Announce Type: replace-cross Abstract: Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training…
Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones
arXiv:2607.17419v1 Announce Type: cross Abstract: Linear attention promises constant-time recurrent inference but degrades sharply on associative recall. We formulate attention…
CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models
arXiv:2607.17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics…
Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models
arXiv:2607.15893v2 Announce Type: replace-cross Abstract: While the internal mechanisms of autoregressive (AR) transformers have been studied extensively, much less is…